Triple

T12327187
Position Surface form Disambiguated ID Type / Status
Subject Kerr County E293861 entity
Predicate countySeat P383 FINISHED
Object Kerrville E226399 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Kerrville | Statement: [Kerr County, countySeat, Kerrville]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kerrville
Context triple: [Kerr County, countySeat, Kerrville]
  • A. Kerrville chosen
    Kerrville is a small city in central Texas known for its scenic Guadalupe River setting, Hill Country landscapes, and vibrant arts and music festivals.
  • B. Corsicana
    Corsicana is a small city in north-central Texas known for its oil boom history and as a regional commercial and transportation hub between Dallas and Houston.
  • C. Beeville
    Beeville is a small city in southern Texas known as the county seat of Bee County and home to Coastal Bend College.
  • D. Lubbock
    Lubbock is a major city in northwest Texas known for its role as an agricultural, educational, and economic hub of the region.
  • E. Wimberley
    Wimberley is a small, scenic town in central Texas known for its picturesque Hill Country landscapes, swimming holes, and artsy, tourist-friendly downtown.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d6ab6ae0dc8190b1522a9c1c55c114 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f4f90a881908c5060dd197744d1 completed April 10, 2026, 6:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6c0d431308190be15e9d9dbee1eaf completed May 3, 2026, 3:28 a.m.
Created at: April 8, 2026, 9:53 p.m.